论文标题
利用长短期记忆神经网络的数字相干系统中的纤维非线性补偿
Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks
论文作者
论文摘要
我们首次介绍了长期记忆（LSTM）神经网络体系结构的利用，以补偿数字相干系统中的纤维非线性。我们对单个通道和多渠道16-QAM调制格式进行了偏振多路复用的C波段或O波段传输系统进行数值模拟。为了揭示基于LSTM的接收器在性能和复杂性方面的限制，对隐藏单元数量和训练LSTM算法并相对应的符号的效果的详细分析以及训练LSTM算法的符号长度。数值结果表明，LSTM神经网络可以作为光学接收器的后处理器非常有效，这些处理器对纤维中非线性损害的数据进行了分类，并且与数字背部传播相比，尤其是在多通道传输方案中，并且提供了卓越的性能。复杂性分析表明，随着隐藏单元的数量和通道存储器增加的长距离（> 1000 km）的复杂程度可能不那么复杂，LSTM变得更加复杂。
We introduce for the first time the utilization of Long short-term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C-band or O-band transmission systems for single channel and multi-channel 16-QAM modulation format with polarization multiplexing. A detailed analysis regarding the effect of the number of hidden units and the length of the word of symbols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be very efficient as post processors of optical receivers which classify data that have undergone non-linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi-channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase can be less complex than DBP in long distances (> 1000 km).
